RV144 analysis

knitr::opts_knit$set(root.dir = "/Users/amitmeir/Documents/rglab/flowReMix")

Loading Data

library(flowReMix)

getExpression <- function(str) {
  first <- substr(str, 1, 7)
  second <- substr(str, 8, nchar(str))
  second <- strsplit(second, "")[[1]]
  seperators <- c(0, which(second %in% c("-", "+")))
  expressed <- list()
  for(i in 2:length(seperators)) {
    if(second[seperators[i]] == "+") {
      expressed[[i]] <- paste(second[(seperators[(i - 1)] + 1) : seperators[i]], collapse = '')
    }
  }
  expressed <- paste(unlist(expressed), collapse = '')
  expressed <- paste(first, expressed, sep = '')
  return(expressed)
}
# Loading Data --------------------------------
# hvtn <- read.csv(file = "data/merged_505_stats.csv")
# names(hvtn) <- tolower(names(hvtn))
# hvtn <- subset(hvtn, !is.na(ptid))
# saveRDS(hvtn, file = "data/505_stats.rds")
# Getting Demographic data ------------------------
demo <- read.csv(file = "data/primary505.csv")
infect <- data.frame(ptid = demo$ptid, status = demo$HIVwk28preunbl)
infect <- subset(infect, infect$ptid %in% hvtn$ptid)
# Getting marginals -----------------------------
library(flowReMix)
hvtn <- readRDS(file = "data/505_stats.rds")
length(unique(hvtn$name))
length(unique(hvtn$ptid))
length(unique(hvtn$population))
unique(hvtn$population)
unique(hvtn$stim)
nchars <- nchar(as.character(unique(hvtn$population)))
#marginals <- unique(hvtn$population)[nchars < 26]
marginals <- unique(hvtn$population)[nchars == 26]
marginals <- subset(hvtn, population %in% marginals)
marginals <- subset(marginals, stim %in% c("negctrl", "VRC ENV A",
                                           "VRC ENV B", "VRC ENV C",
                                           "VRC GAG B", "VRC NEF B",
                                           "VRC POL 1 B", "VRC POL 2 B"))
marginals <- subset(marginals, !(population %in% c("4+", "8+")))
marginals <- subset(marginals, !(population %in% c("8+/107a-154-IFNg-IL2-TNFa-", "4+/107a-154-IFNg-IL2-TNFa-")))
marginals$stim <- factor(as.character(marginals$stim))
marginals$population <- factor(as.character(marginals$population))
# Descriptives -------------------------------------
library(ggplot2)
marginals$prop <- marginals$count / marginals$parentcount
# ggplot(marginals) + geom_boxplot(aes(x = population, y = log(prop), col = stim))
require(dplyr)

negctrl <- subset(marginals, stim == "negctrl")
negctrl <- summarize(group_by(negctrl, ptid, population), negprop = mean(prop))
negctrl <- as.data.frame(negctrl)
marginals <- merge(marginals, negctrl, all.x = TRUE)
# ggplot(subset(marginals, stim != "negctrl" & parent == "4+")) +
#   geom_point(aes(x = log(negprop), y = log(prop)), size = 0.25) +
#   facet_grid(stim ~ population, scales = "free") +
#   theme_bw() +
#   geom_abline(intercept = 0, slope = 1)
# Setting up data for analysis ---------------------------
unique(marginals$stim)
gag <- subset(marginals, stim %in% c("VRC GAG B", "negctrl"))
gag$subset <- factor(paste("gag", gag$population, sep = "/"))
gag$stimGroup <- "gag"
pol <-subset(marginals, stim %in% c("negctrl", "VRC POL 1 B", "VRC POL 2 B"))
pol$subset <- factor(paste("pol", pol$population, sep = "/"))
pol$stimGroup <- "pol"
env <- subset(marginals, stim %in% c("negctrl", "VRC ENV C", "VRC ENV B", "VRC ENV A"))
env$subset <- factor(paste("env", env$population, sep = "/"))
env$stimGroup <- "env"
nef <- subset(marginals, stim %in% c("negctrl", "VRC NEF B"))
nef$subset <- factor(paste("nef", nef$population, sep = "/"))
nef$stimGroup <- "nef"
subsetDat <- rbind(gag, pol, env, nef)
subsetDat$stim <- as.character(subsetDat$stim)
subsetDat$stim[subsetDat$stim == "negctrl"] <- 0
subsetDat$stim <- factor(subsetDat$stim)
# Converting subset names ------------------
subsets <- as.character(unique(subsetDat$subset))
expressed <- sapply(subsets, getExpression)
map <- cbind(subsets, expressed)
subsetDat$subset <- as.character(subsetDat$subset)
for(i in 1:nrow(map)) {
  subsetDat$subset[which(subsetDat$subset == map[i, 1])] <- map[i, 2]
}
subsetDat$subset <- factor(subsetDat$subset)
# Getting outcomes -------------------------------
treatmentdat <- read.csv(file = "data/rx_v2.csv")
names(treatmentdat) <- tolower(names(treatmentdat))
treatmentdat$ptid <- factor(gsub("-", "", (treatmentdat$ptid)))
treatmentdat <- subset(treatmentdat, ptid %in% unique(subsetDat$ptid))
# Finding problematic subsets?
keep <- by(subsetDat, list(subsetDat$subset), function(x) mean(x$count > 1) > 0.02)
keep <- names(keep[sapply(keep, function(x) x)])
#result$subsets[result$qvals < 0.1] %in% keep
subsetDat <- subset(subsetDat, subset %in% keep)

Loading Model

load(file = "Data Analysis/results/HVTNclust2.Robj")

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ROC Table for Vaccination

ROC Table for Infection

vaccine <- outcome[, 2] == 0
hiv <- infect[, 2]
hiv[vaccine == 0] <- NA
infectROC <- rocTable(fit, hiv, direction = ">", adjust = "BH",
                      sortAUC = FALSE)
infectROC[order(infectROC$auc, decreasing = TRUE), ]

Scatter Plot: Figure too larget to fit here…

scatter <- plot(fit, target = vaccine, type = "scatter", ncol = 11)

FDR Curves: Figure too larget to fit here…

vaccination <- outcome[, 2] == 0
fdrplot <- plot(fit, target = vaccination, type = "FDR")

Functionality and Polyfunctionality Boxplots

nfunctions <- sapply(strsplit(colnames(fit$posteriors)[-1], "+", fixed = TRUE), function(x) length(x) - 1)
weightList <- list()
weightList$polyfunctionality <- weights <- nfunctions / choose(5, nfunctions)
weightList$functionality <- rep(1, length(nfunctions))

subsets <- names(fit$posteriors[, -1])
stim <- sapply(strsplit(subsets, "/"), function(x) x[1])
stimnames <- unique(stim)
stim <- lapply(stimnames, function(x) subsets[stim %in% x])
names(stim) <- stimnames
parent <- sapply(strsplit(subsets, "/"), function(x) x[2])
parentnames <- unique(parent)
parent <- lapply(parentnames, function(x) subsets[parent %in% x])
names(parent) <- parentnames
stimparent  <- sapply(strsplit(subsets, "/"), function(x) paste(x[1:2], collapse = "/"))
stimparentnames <-unique(stimparent)
stimparent <- lapply(stimparentnames, function(x) subsets[stimparent %in% x])
names(stimparent) <- stimparentnames

infection <- hiv
infection[hiv == 0] <- "NON-INFECTED"
infection[hiv == 1] <- "INFECTED"
infection[is.na(hiv)] <- "PLACEBO"
plot(fit, type = "boxplot", groups = stim, weights = weightList, ncol = 2,
     target = infection)
plot(fit, type = "boxplot", groups = parent, weights = weightList, ncol = 2,
                  target = infection)
plot(fit, type = "boxplot", groups = stimparent, weights = weightList, ncol = 3, target = infection)

Random effect model for differnt thresholds

load("data analysis/results/HVTNrand2.Robj")
for(threshold in c(0.5, 0.75, 0.85, 0.9, 0.95, 1)) {
  randplot <- plot(randStability, fill = rocResults$auc,
                  threshold = threshold)
  print(randplot)
}

Ising model for differnt thresholds

load("data analysis/results/HVTNising2.Robj")
for(threshold in c(0.5, 0.75, 0.85, 0.9, 0.95, 1)) {
  isingplot <- plot(stability, fill = rocResults$auc, threshold = threshold)
  print(isingplot)
}